v4.0.0

• Various bug fixes (see release log in README.md)
• Predefined propensity models including:
  • Generic feedforward MLP for continuous and discrete action spaces built in PyTorch
  • xGBoost for continuous and discrete action spaces built in sklearn
  • Both PyTorch and sklearn models can handle space discrete actions spaces i.e., a propensity model can be exposed to 'new' actions provided the full action space definition is provided at the training time of the propensity model
• Metrics pattern with:
  • Effective sample size calculation
  • Proportion of valid weights i.e., the mean proportion of weights between a min and max value across trajectories
• Refactored the BehavPolicy class to accept a 'policy_func' that aligns with the other policy classes